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Learn Agentic AI

Kanishk Patel

Practical breakdowns of agentic systems, AI workflows, and emerging research — for builders, founders, and curious minds.

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Substack
PricingFreemiumPublishesDaily
Issues120Founded10 months agoLast Issue11 days ago
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Latest Issues

What is single-edit counterfactual credit? Your multi-agent team finally learns which change helped

Five sentences to take with you

  1. RoleFlow is a multi-agent system where a small trained router builds a chain of LLM roles and, after each rejected answer, changes it by exactly one edit.

  2. Its training method, SECPO, reruns the sa...

11 days ago
2
1

Your agent's saved lessons are teaching it old mistakes

Five sentences to take with you

  1. Many self-improving agents write “lessons learned” from past runs into memory and reuse them later, without touching model weights.

  2. A new paper argues those lessons recycle their own errors: a bad...

12 days ago

Stop summarizing your agent's context. Drop its cache instead.

Five sentences to take with you

  1. Cache engineering lets an agent edit its own KV cache directly, hiding old entries from attention while the text history stays untouched.

  2. Because the prompt never changes, the server skips recompu...

13 days ago
1

What is self-generated credit? Your agent's own summaries now decide which turns get trained

Five sentences to take with you

  1. Self-Credit is an RL recipe in which a long-horizon agent, while compressing its own context, names the earlier turns that were decisive, and training uses those citations as turn-level credit.

  2. Ea...

15 days ago
2
2

The judge that cost more than the run: a 7B monitor stops doomed coding agents for $0.00024 a check

Five sentences to take with you

  1. StopLoss is a small external monitor that reads a coding agent’s last few tool steps and decides whether the run is heading for failure.

  2. It learns that skill from finished runs alone, labeling eve...

16 days ago
3
1

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  • Kanishk Patel

    I build and advise on AI systems that work in the real world — beyond demos, hype, and isolated experiments.

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